I will discuss privacy-friendly methods for finding good audiences for on-line display advertising, by extracting quasi-social networks from browser behavior on user-generated content sites. Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. I will discuss methods for extracting quasi-social networks from data on visitations to social media pages. The data are completely anonymous with respect to both browser identity and content. I will introduce measures of computing which browsers are "close" to other browsers that in the past have exhibited brand affinity. Results show that audiences with high brand proximity indeed show substantially higher brand affinity themselves, as well as higher propensity to convert. Time permitting, I also will present additional findings relating to whether the the quasi-social network actually embeds a true social network, how to gather appropriate training data, and whether on-line advertising actually is effective. This work was done in collaboration with Michael Barnathan, Brian Dalessandro, Rod Hook, Alan Murray, Claudia Perlich, and Xiaohan Zhang.
Foster Provost is Professor, NEC Faculty Fellow, and Paduano Fellow of Business Ethics (Emeritus) at the NYU Stern School of Business. He is Chief Scientist for Coriolis Ventures, a NYC-based early stage venture and incubation firm. In 2001 he was Program Chair of the KDD Conference, and he just retired as Editor-in-Chief of the journal Machine Learning. His main research interests these days include predictive modeling with (social) network data, and alternative methods for data acquisition for data mining. Foster has applied data mining in practice to applications including on-line advertising, fraud detection, network diagnosis, targeted marketing, counterterrorism, and others. His work has won best paper awards at KDD, IBM Faculty Awards, and a President's Award at NYNEX Science and Technology. Last year his work on social network-based marketing systems won the 2009 INFORMS Design Science Award.